Semi-Partitioned Scheduling of Fork-Join Tasks Using Work-Stealing

This paper explores the behavior of parallel fork-join tasks on multicore platforms by resorting to a semi-partitioned scheduling model. This model offers a promising framework to embedded systems which are subject to stringent timing constraints as it provides these systems with very interesting properties. The proposed approach consists of two stages -- an offline stage and an online stage. During the offline stage, a multi-frame task model is adopted to perform the fork-join task-to-core mapping so as to improve the schedulability and the performance of the system, and during the online stage, work-stealing is exploited among cores to improve the system responsiveness as well as to balance the execution workload. The objective of this work is twofold: (1) to provide an alternative technique that takes advantage of the semi-partitioned scheduling properties by offering the possibility to accommodate fork-join tasks that cannot be scheduled in any pure partitioned environment, and (2) to reduce the migration overhead which has shown to be a traditional major source of non-determinism in global approaches. The simulation results show an improvement of the proposed approach over the state-of-the-art of up to 15% of the average response-time per task set.